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Information Marginalization on Subgraphs

Full Text: subgraph.pdf PDF

Real-world data often involves objects that exhibit multiple relationships; for example, `papers' and `authors' exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. `papers'), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data, achieving good results in many problems.

Citation

J. Huang, T. Zhu, R. Greiner, D. Zhou, D. Schuurmans. "Information Marginalization on Subgraphs". European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, September 2006.

Keywords: complex networks, citation references, multiple sources, machine learning
Category: In Conference

BibTeX

@incollection{Huang+al:PKDD06,
  author = {Jiayuan Huang and Tingshao Zhu and Russ Greiner and Dengyong Zhou
    and Dale Schuurmans},
  title = {Information Marginalization on Subgraphs},
  booktitle = {European Conference on Principles and Practice of Knowledge
    Discovery in Databases (PKDD)},
  year = 2006,
}

Last Updated: April 23, 2007
Submitted by Nelson Loyola

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